Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN 55455, USA.
IEEE Trans Med Imaging. 2010 Oct;29(10):1759-67. doi: 10.1109/TMI.2010.2052063. Epub 2010 Jun 7.
Magnetoacoustic tomography with magnetic induction (MAT-MI) is an imaging approach proposed to conduct noninvasive electrical conductivity imaging of biological tissue with high spatial resolution. In the present study, based on the analysis of the relationship between the conductivity distribution and the generated MAT-MI acoustic source, we propose a new multi-excitation MAT-MI approach and the corresponding reconstruction algorithms. In the proposed method, multiple magnetic excitations using different coil configurations are employed and ultrasound measurements corresponding to each excitation are collected to derive the conductivity distribution inside the sample. A modified reconstruction algorithm is also proposed for the multi-excitation MAT-MI imaging approach when only limited bandwidth acoustic measurements are available. Computer simulation and phantom experiment studies have been done to demonstrate the merits of the proposed method. It is shown that if unlimited bandwidth acoustic data is available, we can accurately reconstruct the internal conductivity contrast of an object using the proposed method. With limited bandwidth data and the use of the modified algorithm we can reconstruct the relative conductivity contrast of an object instead of only boundaries at the conductivity heterogeneity. Benefits that come with this new method include better differentiation of tissue types with conductivity contrast using the MAT-MI approach, specifically for potential breast cancer screening application in the future.
磁感应磁声断层成像(MAT-MI)是一种提出的成像方法,用于对生物组织进行非侵入式电导率成像,具有高空间分辨率。在本研究中,基于对电导率分布与产生的 MAT-MI 声源之间关系的分析,我们提出了一种新的多激发 MAT-MI 方法和相应的重建算法。在提出的方法中,使用不同的线圈配置进行多次磁激发,并采集与每个激励相对应的超声测量值,以推导出样品内部的电导率分布。当仅可获得有限带宽的声测量值时,还提出了一种用于多激发 MAT-MI 成像方法的改进重建算法。已经进行了计算机模拟和体模实验研究,以证明所提出方法的优点。结果表明,如果可获得无限带宽的声数据,则可以使用所提出的方法准确地重建物体的内部电导率对比度。如果使用有限带宽的数据和改进的算法,则可以重建物体的相对电导率对比度,而不仅仅是电导率异质性的边界。这种新方法的优点包括使用 MAT-MI 方法更好地区分具有电导率对比度的组织类型,特别是将来在乳腺癌筛查应用方面。